Parkinson's disease (PD) has been associated with facial expression recognition difficulties. However, this impairment could be secondary to the one produced in other cognitive processes involved in recognition, such as selective attention. This study investigates the influence of two selective attention components (inhibition and visual search) on facial expression recognition in PD. We compar...

Simulation models of expression recognition contend that to understand another's facial expressions, individuals map the perceived expression onto the same sensorimotor representations that are active during the experience of the perceived emotion. To investigate this view, the present study examines facial expression and identity recognition abilities in a rare group of participants who show f...

In this paper, we propose a face recognition algorithm using AAM and Gabor features. Gabor feature vectors which are well known to be robust with respect to small variations of shape, scaling, rotation, distortion, illumination and poses in images are popularly employed for feature vectors for many object detection and recognition algorithms. EBGM, which is prominent among face recognition algo...

Automatic facial expression recognition for novel individuals from 3D face data is a challenging task in pattern analysis. This paper describes a feature selection process for pose-invariant 3D facial expression recognition. The process provides a lower dimensional subspace representation, which is optimized to improve the classification accuracy, retrieved from geometrical localization of faci...

Previous efforts at facial expression recognition have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static facial “mugshots.” In this paper we develop new, more accurate representations for facial expression by building a video database of facial expressions and then probabilistically characterizin...

Facial expression provides an important behavioural measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we are evaluating the performance of PCA and LDA to recognize seven diffe...

This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a c...

Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS) sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP) and the ra...

One of the crucial features defining basic emotions and their prototypical facial expressions is their value for survival. Childhood traumatic experiences affect the effective recognition of facial expressions of negative emotions, normally allowing the recruitment of adequate behavioral responses to environmental threats. Specifically, anger becomes an extraordinarily salient stimulus unbalanc...

Automatic facial expression recognition has gained much attention during the last decade because of its potential application in areas such as more engaging human-computer interfaces. Automatic facial expression recognition is a sub-area of face analysis research that is based heavily on methods of computer vision, machine learning, and image processing. Many efforts either to create a novel or...